Fully-funded PhD scholarship:
A Wearable Sensor System for In Vivo Measurement of Joint Loads at the Hip and Knee during Stair Ascent and Descent

ABOUT THE PROJECT

We are seeking a highly motivated PhD candidate to join an innovative project at the cutting edge of biomechanics, wearable technology, and artificial intelligence. This project aims to develop and validate a novel wearable sensor system that uses machine learning to estimate hip and knee joint loads during daily activities like stair climbing.

Building on our successfully patented method and previous data, the candidate will work with a unique, instrumented staircase—the only one of its kind in Australia—to collect high-fidelity biomechanical data. The research will involve developing advanced mathematical models to translate data from wearable inertial sensors (IMUs) into accurate estimates of joint loading, enabling real-world mobility monitoring and personalised gait retraining.

This is a fully-funded opportunity to contribute to a project with significant potential for real-world impact in digital health and chronic disease management.

WHAT DOES THE SCHOLARSHIP PROVIDE?

The successful applicant will receive;

• A tax-free stipend of $35,188 (AUD) per annum for up to 3 years to support living costs, supported by the Research Training Program (RTP) Fee Offset.

This is available to domestic candidates (Australian/New Zealand citizens or Australian permanent residents).

• Financial support for conference attendance, fieldwork, and other approved research-related costs.

• Access to the state-of-the-art Human Movement Lab at Western Sydney University, featuring Australia's only instrumented staircase, providing a unique national capability for advanced biomechanics research.

ELIGIBILITY CRITERIA

The successful applicant should:

• hold qualifications and experience equal to one of the following

(i) an Australian First Class Bachelor (Honours) degree,

(ii) coursework Masters with at least 25% research component,

(iii) Research Master’s degree, or (iv) equivalent overseas qualifications.

• Demonstrate strong academic performance in subjects related to biomechanics, programming, data analysis, or signal processing.

• Have demonstrated programming experience (e.g., Python, MATLAB) for data analysis. Familiarity with machine learning frameworks is highly desirable.

• Be enthusiastic and highly motivated to pursue advanced-level study both independently and as part of a collaborative team.

• Have excellent problem-solving skills and a keen interest in applying technology to healthcare challenges.

• While not essential, experience with biomechanical data collection (motion capture, IMUs) or knowledge of human anatomy will be considered advantageous.

HOW TO APPLY

1. Review the project’s eligibility criteria. You will need to provide in your application a document which explains how you satisfy the projects eligibility criteria.

2. Contact the lead researcher listed to discuss your eligibility, the project’s requirements and your intention to apply. You should email them to introduce yourself, describe your qualifications and experience, and express your interest in their research project. If they are interested, you may want to arrange a phone call, video call or meeting to discuss your application. You will need to request a letter of support from the lead researcher to support your application for the scholarship.

3. Submit an online application for the project by logging into the Online Portal and selecting 'Project Scholarship'.

Note: You do not need to submit an application for Doctor of Philosophy or Research Scholarship.

Incomplete applications or applications that do not conform to the above requirements will not be considered.

For questions and advice about the research project, please contact the Lead Researchers; Prof. Roy Cheung: [email protected] and Dr. Leo Zhang: [email protected]

For general admission enquiries, please email [email protected]

Applications close 31 October 2025

• Applications close at 11.59pm Australian Eastern Daylight Time (AEDT).

• Scholarship reference code: PS2025_CS0717752_SoHS